Optimal Task Allocation and Distributed Sensing in Collective Autonomous Robotics
William Agassounon, Alcherio Martinoli, Rodney Goodman

Our research aims at studying two particular topics within the Collective Robotics field, these are the division of labor and the dynamic task allocation. The Swarm Intelligence approach can be applied to fully distributed systems that consist of several autonomous decision making entities working together with minimal communication and local perception to complete one or several tasks. Our approach is inspired by biological systems such as colonies of social insects (ants, bees, termites, etc) in which the collective behavior often emerges from a series of local agent-to-agent and agent-to-environment interactions. We are developing response threshold-based algorithms for optimal task allocation and probabilistic models that provide accurate forecast of the resulting collective behavior. Finally, one of the main strengths of this project is the attempt to create a theoretical framework for real embedded systems provided with threshold allocation mechanisms. These systems are therefore analyzed at several implementation levels, from analytical probabilistic models to real robots experiments through embodied sensor-based simulators. (full report)

Awareness-Based Computation
George Barbastathis, Greg Billock, Demetri Psaltis, Christof Koch

In this project, we are developing design principles for intelligent systems that can interact with very complex, variable, and poorly modeled environments. In doing so, we draw inspiration from the discoveries of neurobiology relating to the role of attention and awareness. These aspects of biological processing systems is key in conferring on them the ability to function in such high-dimensional real-world environments. At the heart of our architecture lies the idea of adapting an abstraction of awareness with which to endow artificial man-made systems. (full report)

Configurable Architectures and Systems for Real-Time Low-level Vision
Arrigo Benedetti, Pietro Perona

The long-term goal of this project is to build an infrastructure for the design and implementation of real-time computer vision systems. Since vision algorithms are compute-bound we have chosen the technology of Field Programmable Gate Array (FPGAs), that allow to exploit the parallelism inherent to the first stages of low-level vision tasks. The first problem that we have considered is the real-time computation of the optical flow measured from the sequence of images captured by a video camera. We have designed, built and demonstrated a system able to select in real-time 2-D visual features on a commercially available platform. During this process we have learned that the system level architectures of commercially available configurable systems are not optimized for low level vision tasks, therefore, we have designed a novel architecture dedicated to real-time processing of video streams. A system based on this architecture has been built and is currently being tested. More recently, we have studied the problem of bit-width computation for the optimization of the data paths found in digital video signal processors. (full report)

Awareness-Based Computation: The Bin Packing Problem
Greg Billock, Demetri Psaltis, Christof Koch

In previous work (see report entitled Awareness-Based Computation), we have investigated the impact of using approaches to simulated environments/problems inspired by the way human beings use awareness and attentional mechanisms to interact with a complex world. In this work, we explore how this works in the context of a familiar computer science problem: bin packing. As an abstract problem, the bin-packing problem has the advantage of having been subjected to extensive analysis and so much is known about it. It is a very important practical problem, as well, with applications to cutting stock, machine and job scheduling, parallel processing scheduling, FPGA layout, loading problems, and more. By using ideas about reduced representations of what is most important in an on-line solution of the problem, we are able to devise a heuristic which outperforms existing heuristics, and understand how and why it does so. (full report)

3D Photography on Your Desk
Jean-Yves Bouguet, Pietro Perona

We are developing a simple and inexpensive method for extracting the three-dimensional shape of objects by using weak-structured lighting. Experimental results demonstrate that the error in reconstructing the surface is less than 1%. (full report)

Evolving Robust, Collective Patrolling Behavior Using Genetic Algorithms
Joseph Chen, Alcherio Martinoli, Rodney M. Goodman

Evolution is a powerful force. Harvester ants have successfully evolved to efficiently patrol their territory for different type of events (food items, enemy intrusion, etc.). The goal of this project is to study how effective and robust patrolling behavior can be evolved first in embodied, sensor-based simulations and then in real robot experiments. We will use evolutionary techniques (Genetic Algorithms, GA) for exploring the individual control parameters that play a crucial role in the team patrolling performance. In order to better understand the required individual and group capabilties for effective patrolling, we will test the influence of individual navigation capabilities and different fitness functions. We will also note whether any interesting collective behavior develops if the robots are allowed to directly communicate at each encounter, without introducing any type of stigmergic mechanism (e.g. pheromones). (full report)

Distributed Collective Building of Two-Dimensional Structures Using Autonomous Robots
Kjerstin Easton, Alcherio Martinoli, Rodney Goodman

Using autonomous robots to build three-dimensional structures is a distant goal, but the first step in approaching collective building is to construct two-dimensional architectures. Using a team of miniature Khepera robots with manipulation and vision capabilities, we will implement a building technique modeled after qualitative stigmergic construction mechanisms used by social insects. This technique will allow the robots to communicate building instructions through modifications to the local environment, avoiding dependence on explicit robot-to-robot communication and lending itself to implementation with any number of robots. (full report)

Distributed Turbulent Flow Control by Neural-Networked MEMS
Zhigang Han, Qiao Lin, Xuan-Qi Wang, Fukang Jiang, Thomas Tsao, Yu-Chong Tai
Collaborators: Vincent Koosh (Caltech), Rodney Goodman (Caltech), James Lew (MAE, UCLA) , Chih-Ming Ho (MAE, UCLA)

The ultimate goal of this project is to develop fully integrated MEMS with microsensors, microactuators, and microelectronics (M³) for turbulent boundary layer control. We have developed many generations of MEMS shear-stress sensors for vortex detection. The latest one is a fully integrated shear-stress sensor using a post-IC process that is added onto foundry-processed CMOS wafers. This shear-stress sensor uses a gate-polysilicon hot-wire as the sensing element that sits on a freestanding Parylene diaphragm suspended over a cavity. A special Parylene vacuum sealing and etch back process is used to achieve better thermal isolation and overall sensitivity. Wind tunnel testing of this sensor shows a sensitivity of 30 mV/Pa and a measured bandwidth of 18 kHz. We have also performed extensive theoretical analysis of these sensors. The resulting 2D MEMS shear-stress sensor theory, which includes heat transfer effects ignored by the classical theory, is verified by experimental data. We also perform 3-D heat transfer simulation and the results agree with the testing data and support the proposed new theory. (full report)

Guiding a Robot with an Analog VLSI Motion Sensor Based on the Visual System of the Fly
Reid Harrison, Christof Koch

Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land safely. Mobile robots are ideal candidates for using this sensory modality to enhance their performance, but so far have been limited by the computational expense of processing video. Also, the complex structure of natural visual scenes poses an algorithmic challenge for extracting useful information in a robust manner. We address both issues by creating a small, low-power visual sensor with integrated analog parallel processing to extract motion in real-time. Because our architecture is based on biological motion detectors, we gain the advantages of this highly evolved system: a design that robustly and continuously extracts relevant information from its visual environment. We show that this sensor is suitable for use in the real world, and demonstrate its ability to compensate for an imperfect motor system in the control of an autonomous robot. The sensor attenuates open-loop rotation by a factor of 31 with less than 1 mW power dissipation. (full report)

Distributed Plume Tracing
Adam T. Hayes, Alcherio Martinoli, Owen Holland, Rodney M. Goodman

The objective of this project is to study biologically inspired algorithms which enable a robot or group of robots to track an odor plume to its source, with an appropriate combination of speed, efficiency, reliability, and accuracy. Research is conducted at three levels: non-embodied point simulations, embodied sensor-based simulations, and real robots. The simulations use sensors and actuators which are based on the capabilities of the real robots, and plume information is derived from empirical data files recorded from real plumes or realistic plume simulators. In simulation we explore the performance of various families of simple algorithms, as well as the potential for automated parameter tuning and on-line learning. We assess the most promising algorithms on real robots, which are equipped with Caltech olfactory sensors, anemometric devices, and simple communication systems. (full report)

Actuated Surgical Endoscopes for Minimally Invasive Surgery
Hans D. Hoeg, Joel W. Burdick, A. B. Slatkin

Our effort is aimed at developing articulated surgical endoscopes that can access the interior of the human body in a minimally invasive manner for the purposes of visualization, diagnosis and therapeutic intervention. We have specifically focused on design and construction of scopes for use in brain surgery and gastrointestinal procedures. (full report)

Super Manueverable UAV Controlled by M³ System
Fukang Jiang, Charles Grosjean, Yong Xu, Yu-Chong Tai
Collaborators: Chih-Ming Ho (MAE, UCLA), Ray Morgan, Martyn Cowley, Scott Newbert (AeroVironment Inc.)

An aircraft for the future - having no tail, controlled by M³ systems, and with no traditional control surfaces - will be developed for low altitude surveillance. A new robust system of distributed microsensors and microactuators, with associated microelectronics (a M³ system) will be designed and fabricated to satisfy flight test requirements. A new aircraft will be designed from scratch to accentuate the concept of achieving aerodynamic maneuvering through a micromachine-based deformable smart surface. This new aircraft design concept can significantly reduce weight, overall power consumption and radar cross-section. (full report)

Psychobiophysics of Transcranial Magnetic Stimulation
Yukiyasu Kamitani, Shinsuke Shimojo

We investigate the relationship between human visual experience and underlying neuronal electrical activity, using transcranial magnetic stimulation (TMS). We explore methods that make the effect of TMS on the visual cortex directly visible, to look at purely cortical activity underlying our conscious visual experience. We also develop a biophysical theory to simulate the effect of magnetic stimulation on single neurons. Based on it, we create compartmental models of realistic cortical neurons to find neural activity underlying perceptual effects of TMS. (full report)

VLSI Implementation of a Neural Network
Vincent Koosh, Rodney Goodman

We are developing a single chip solution to implement a feedforward neural network and training algorithm. (full report)

Neural Coding of Electric Field Amplitude Modulations in Eigenmannia Electric Fish
Gabriel Kreiman, Ruediger Krahe (Department of Biology, U.C. Riverside), Fabrizio Gabbiani, Walter Metzner (Department of Biology, U.C. Riverside), Christof Koch

We are using the electric fish as a model to study the encoding of time-varying signals by single and multiple neurons. Our approach combines signal detection and information-theoretic ideas to quantify the amount of information conveyed by sensory afferents and its targets about amplitude modulations in the electric field. We have shown that single sensory neurons robustly encode a significant proportion of the incoming signal while the pyramidal cell targets extract specific features of the signal. We are currently performing a quantitative study of the possibility of extracting these features by coincidence detection of two pyramidal cells. (full report)

Visual Sensor With Resolution Enhancement by Mechanical Vibrations
Oliver Landolt, Ania Mitros, Christof Koch

The resolution of both biological and man-made vision systems is limited by the finite spacing between receptors. This limit can be overcome by applying continuous low-amplitude vibrations to the image or taking advantage of existing vibrations in the environment. Some animals rely on this principle for improved visual resolution. We are applying it to a novel CMOS visual sensor to increase resolution and decrease fixed pattern noise. (full report)

Micromachined Gyroscope Using Operating Principles from the Fly's Halteres
Oliver Landolt, Zhigang Han, Christof Koch, Yu-Chong Tai

We are developing a surface micromachined 2D angular velocity sensor -- also known as gyroscope -- with the intention of minimizing power consumption. By using a detection principle inspired by the fly's haltere system, we expect our sensor to tolerate a higher noise level than previous designs for detecting the direction of the axis of rotation, thereby enabling a significant reduction of supply voltage and power consumption. Another feature is that the mechanical structure will be fabricated with a material called parylene using a novel technology developed in-house. The target application is flight control in extremely small air vehicles. (full report)

Holographic Imaging of Biological Samples
Wenhai Liu, Demetri Psaltis

We are developing an imaging system with the ability of imaging a 3D object plus the color spectrum information. It makes use of the spatial and wavelength selectivity of volume holograms, which act as multiple lens and color filters to separate the 2D slices with different color from the 3D object into various detectors. It will be a powerful tool for imaging application in cell biology, biochemistry, material research and any other 3D imaging application. (full report)

Support Vector Machines - A New Approach to Learning
Malik Magdon-Ismail, Jennie Yoder, Yaser Abu-Mostafa

Support Vector Machines are a method of extracting information from few noisy data points. A classification boundary is created allowing the largest possible margin of error. The technique is robust and easily implemented. (full report)

Sensing and Control for Robotic Fish Locomotion
Richard Mason, Joel Burdick

We are studying issues in fluid mechanics, nonlinear control, and sensing that are necessary for the development of self-propelled robot fish. (full report)

Micromachined Fluidic Couplers
Ellis Meng, Shuyun Wu, and Yu-Chong Tai

Several types of silicon fluidic couplers have been designed, fabricated, and tested for the purpose of facilitating external connections to MEMS fluidic devices. By using both bulk micromachining and DRIE techniques, couplers of various geometries have been produced for use with any standard MEMS fluidic port. Furthermore, couplers exhibit excellent performance, having an operating range of at least 0-1300 psi. (full report)

Optically Programmable FPGA Systems
Jose Mumbru, Gan Zhou, Arrigo Benedetti, Xin An, George Panotopoulos, Fai Mok, Demetri Psaltis, Pietro Perona

Reconfigurable processors bring a new computational paradigm where the processor modifies its structure to suit a given application, rather than having to modify the application to fit the device. The Optically Programmable Gate Array (OPGA), an enhanced version of a conventional FPGA, utilizes a holographic memory accessed by an array of VCSELs to program its logic. Combining spatial and shift multiplexing to store the configuration pages in the memory, the OPGA module is very compact and has extremely short configuration time allowing for dynamic reconfiguration. The reconfiguration capability of the OPGA can be applied to solve more efficiently problems in pattern recognition and searches in databases. (full report)

Visual Input for Pen-Based Computers
Mario E. Munich, Pietro Perona

Our work focuses on the development of a visual interface for pen-based computers. We are building a system that visually tracks the trajectory of a pen in real-time and recovers the handwritten strokes with sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. (full report)

Camera-Based ID Verification by Signature Tracking
Mario E. Munich, Pietro Perona

The goal of this project is to develop a vision-based biometric technique based on visual capturing of signatures and to evaluate the performance of the system. (full report)

Set-Valued Analysis for Switching Systems
Todd Murphey, Joel W. Burdick

Conventional nonholonomic motion planning and control theories do not directly apply to "overconstrained vehicles,'' such as the Sojourner vehicle of the Mars Pathfinder mission. This research investigates some basic issues that are necessary to build a motion planning and control framework for this potentially important class of mobile robots. A power dissipation approach is used to model the governing equations of overconstrained vehicles that move quasi-statically. These equations are shown to be switched hybrid systems. Standard notions, such as the Lie bracket, are extended to these switched systems. We then develop a controllability test for such systems. We explore motion planning primitives in the context of simplified examples. Another application area is that of distributed manipulation, where parts are being oriented by a large array of actuators. Here, too, the issues of discrete behavior as the part traverses different contact states plays a large role in analyzing stability. (full report)

The Bin Model for Generalization
Alexander Nicholson, Xubo Song, Yaser Abu-Mostafa

The problem of overfitting the data is attacked by using the Bin Model analysis. This provides a method of bounding generalization error without sacrificing valuable training data. (full report)

Learning in Hardware
Alexander Nicholson, Arrigo Benedetti, Yaser Abu-Mostafa, Pietro Perona

We investigate the use of learning and adaptation for digital hardware design. We use reconfigurable hardware devices and discrete optimization methods to learn circuits from a set of examples. We have shown that this approach works well for the design of small arithmetic circuits and that significant performance improvements may be achieved by moving away from a strictly evolvable (genetic algorithms) approach. (full report)

Little Piece of Cortex
George Panotopoulos, Demetri Psaltis, Pietro Perona

We introduce a model of the V1 cortex. This model is composed by an initial filter stage and two interaction stages, inspired by their biological counterparts. The model produces results matching the ones obtained by physiological experiments. (full report)

Hand Gesture Biometrics
George Panotopoulos, Demetri Psaltis

We introduce a biometric measure based on hand gestures. We use simple filters to extract features from a gesture captured in the form of still frames. We then use PCA to perform classification using these features. For small databases we obtain 100% correct classification. (full report)

Divide and Conquer Strategy for Recognition
George Panotopoulos, Demetri Psaltis

We devised a classification strategy based on the division of a single complex question to more, simpler questions. We showed that this strategy corresponds to a tree structure and can be implemented by reconfigurable computers. We demonstrated the efficiency of this strategy on the problem of classification of handwritten digits. We derived analytical expressions linking the performance of the overall classifier to the performance of its parts. (full report)

A CMOS Imager with Focal-Plane Computation for Feature Detection
Alberto Pesavento and Christof Koch

We designed and tested the first CMOS imager with analog VLSI focal-plane computation for feature detection. The chip implements a modified version of the Tomasi-Kanade algorithm that is suitable for integration in a compact analog VLSI chip. The chip has an array of 8 by 8 pixels and uses few microW of power per pixel. (full report)

Microbat
Nick Pornsin-Sirirak, Yu-Chong Tai
Collaborators: Hany Nassef (UCLA), Chih-Ming Ho (UCLA), Joel Grasmeyer (AeroVironment), Matt Keennon (AeroVironment)

Through the discovery of flapping-wing (unsteady-state) aerodynamics, the world's first electric-powered palm-sized ornithopter has been successfully developed and test-flown. This effort is enabled by the use of a new titanium-alloy MEMS (Micro-Electro-Mechanical Systems) airframe/wing technology to produce light but robust 3-D wings. Parylene-C is used as wing membrane. This new wing design results in a 40% wing area reduction compared to the 1st generation wing. We have built a system that includes a lightweight NiCd battery and an electrical motor, a gearbox transmission design of 22:1 gear ratio with 90% efficiency, and a DC-to-DC voltage converter. Together, it allows us to design a complete system with necessary components within the weight budget for a successful flight. So far, the best flight duration obtained by Microbat was 18 seconds. It is mainly limited by the power source. (full report)

Minimal Data Set Optimal Classification
James R. Psota, Malik Magdon-Ismail, Yaser Abu-Mostafa

We are developing classification techniques to detect the nature of a pump malfunction given pump vibration sensor data. The size of the data set is very minimal, creating the need for an extremely robust classifier that incorporates all available information. We investigated several generalized nearest neighbor and Bayesian classifiers. By incorporating hints, or information about the problem known independently of the data set, we show that performance can be significantly improved. (full report)

3D Vision with Minimal Equipment
Silvio Savarese, Jean-Yves Bouguet, Pietro Perona

The aim of our work is to investigate new approaches for three-dimensional reconstruction of objects. The proposed techniques require minimal and inexpensive equipment. (full report)

Perception and 3D Reconstruction of Specular Surfaces
Silvio Savarese, Pietro Perona

The aim of our work is to investigate how the human visual system perceives specular surfaces and which cues can be used to recover the shape of such class of objects. (full report)

Toward Prosthetic Systems Controlled by Parietal Cortex
Krishna Shenoy, Sohaib Kureshi, Richard Andersen, Shiyan Cao, Joel W. Burdick

At present there are no satisfactory treatments or assistive aids for people suffering from neurological disorders such as stroke, ALS, or spinal cord injuries. Neuroscientists have taken great strides in the past few decades toward uncovering basic principles underlying our ability to see and move. The combination of these discoveries and the revolutionary advances in computer technology have led to an emerging view that neural prosthetics --- or electronic interfaces with the brain --- may one day be possible. This project aims to demonstrate the potential for neural prosthetics to help patients with upper spinal cord injury, which results in the loss of arm movements. Andersen and colleagues recently discovered a cortical area in monkeys and humans that encodes the next intended arm movement. This area is ideally suited to provide high-level control signals for guiding real or prosthetic arms. We propose to implant chronic electrode arrays in this region of monkey cortex and to record neural activity generated during reaching arm movements. We will process these neural signals in real-time to construct control signals for guiding a prosthetic arm. Combining behaving-monkey electrophysiology techniques, state-of-the-art electrode array technology, and feedback control systems should provide the foundation on which to build neural prosthetics for humans. Below we outline our major aims and, in the achievements section, we describe our progress in the past year. (full report)

Monotonicity Hints in Machine Learning
Joseph Sill, Yaser Abu-Mostafa

This project focuses on both practical and theoretical aspects of the monotonicity constraint in machine learning. Learning methods which enforce monotonicity in models such as neural networks are being developed. In addition, the flexibility and expressive power of the class of monotonic binary output functions are analyzed and quantified from a theoretical perspective. (full report)

Detection of Human Motion in a Cluttered Scene
Yang Song, Xiaolin Feng, Luis Goncalves, Pietro Perona

Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation, nor grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient. (full report)

A 2-D Change Detection and Postitioning System Analog VLSI
Theron Stanford, Christof Koch

We are designing analog CMOS chips which will extract information about moving objects such as their relative size, position, and velocity. We are using analog circuits because of their high-speed real-time performance. Immediate applications of this type of chip include electronic security systems, on- or off-vehicle sensors for intelligent transportation systems and target detection systems. (full report)

Electronic Nose Project
Samuel Tang, Rodney Goodman

The proposed electronic nose chip is composed of four parts: sensor stage, signal processing stage, database, and classifier. (full report)

Swarm Intelligence and Traffic Safety
Philip Tsao, Alcherio Martinoli, Rodney M. Goodman

An automotive controller that complements the driving experience must work to avoid collisions, enforce a smooth trajectory, and deliver the vehicle to the intended destination as quickly as possible. Unfortunately, satisfying these requirements with traditional methods proves intractable at best and forces us to consider biologically-inspired techniques such as Swarm Intelligence. A controller is currently being designed in a robot simulation program with the goal of implementing the system in real hardware to investigate these biologically-inspired techniques and to validate the results. (full report)

Polymer Based Electrospray Chips for Mass Spectrometry
Xuan-Qi Wang, Amish Desai, and Yu-Chong Tai
Collaborators: Lawrence Licklider, Terry D. Lee (Beckman Research Institute, City of Hope Research Center, Duarte, CA)

We have developed a MEMS system with an overhanging polymer microcapillary 2.5 mm in length and with a 5 µm x 10 µm orifice size at the tip. The fabricated systems have been successfully interfaced with a mass spectrometer (MS) to validate electrospray ionization (ESI) for biochemical analysis. The prediction of a reduction in Taylor cone size has also been observed with real time ESI fluid visualization from our chip. Built-in micro particle filters and centimeter long serpentine microchannels were fabricated on the chip with a low temperature process by using the Parylene polymer as a structural material, aluminum and photoresist as sacrificial layers, and bromine triflouride (BrF3) gas phase etching for final microcapillary releasing. The use of an overhanging polymer structure adds a new a level of mechanical robustness that was never achievable with other thin films. Functionality of our device was proven by consistent detection of Myoglobin in a 200 nM solution at a flow rate of 35nL/min and a voltage potential of 1.5 kV. (full report)

Learning Object Class Models
Markus Weber, Max Welling, Robert Fergus, Pietro Perona

We have developed a method to automatically learn models of visual object classes from sets of unlabeled and unsegmented training images. The method has been demonstrated to work on images of cars and handwritten characters and it is being adapted to human faces. (full report)

Finding Faces in Cluttered Scenes
Markus Weber, Michael Burl, Pietro Perona

We have designed algorithms that learn a probabilistic description of human faces and other object classes. We have implemented a real-time face detection system which runs at 1Hz and demonstrates the ability to handle deformations, occlusions and background clutter. (full report)

MEMS Flow Sensors for Nano-Fluidic Applications
Shuyun Wu, Qiao Lin, Yin Yuen, and Yu-Chong Tai

We have developed micromachined thermal sensors for measuring liquid flow rates in the nanoliter-per-minute range. The sensors use a boron-doped polysilicon thin-film heater that is embedded in the silicon nitride wall of a microchannel. The boron doping is chosen to increase the heater's temperature coefficient of resistance within tolerable noise limits, and the microchannel is suspended from the substrate to improve thermal isolation. The sensors have demonstrated a flow rate resolution better than 1 nL/min, as well as the capability for detecting micro bubbles in the liquid. Heat transfer simulation has also been performed to explain the sensor operation and yielded good agreement with experimental data. (full report)

Micromachined Rubber O-ring Micro-Fluidic Couplers
Tze-Jung Yao, Yu-Chong Tai

The goal of this project is to develop a "quick-connect" for microfluidic devices. We have developed a simple silicone-rubber O-ring MEMS coupler. The MEMS O-ring couplers are easy to fabricate and use, reusable, can withstand high pressure (>60psi), and provide good seals. To demonstrate this concept, a quick-connect coupler between a glass capillary tube and a silicon chip has been fabricated and tested. More than 60 psi seal has been achieved between a glass tube (860 µm O.D.) and a rubber O-ring (400µm I.D.) without measurable leakage. (full report)